We present a methodology for identifying patients who have experienced Paroxysmal Atrial Fibrillation (PAF) among a given subjects population. Our work is intended as an initial step towards the design of an unobtrusive system for concurrent detection and monitoring of chronic cardiac conditions.

Our methodology comprises two stages: off-line training and on-line analysis. During training the most significant features are selected using machine-learning methods, without relying on a manual selection based on previous knowledge. Analysis is based on two phases: feature extraction and detection of PAF patients. Light-weight algorithms are employed in the feature extraction phase, allowing the on-line implementation of this step on wearable and resource-constrained sensor nodes. The detection phase employs techniques borrowed from the field of failure prediction. While these algorithms have found extensive applications in diverse scenarios, their application to automated cardiac analysis has not been sufficiently investigated.

Obtained results, in terms of performance, are comparable to similar efforts in the field. Nonetheless, the proposed method employs computationally simpler and more efficient algorithms, which are compatible with the computational constraints of state-of-the-art body sensor nodes.